通过偏好模型评估数据质量

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS ACM Journal of Data and Information Quality Pub Date : 2023-11-29 DOI:10.1145/3632407
Julian Le Deunf, Arwa Khannoussi, Laurent Lecornu, Patrick Meyer, John Puentes
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引用次数: 0

摘要

评估数据质量是一个多维度的问题,通常取决于数据的预期用途或最终目的。许多著作都探讨了各种应用领域中众所周知的数据质量维度规范,但却没有解决决策支持中质量属性的相互依赖和聚合问题。因此,在这项工作中,我们提出了一个与上下文相关的正式流程来评估数据质量,该流程整合了多标准决策辅助领域的偏好模型。该偏好模型的参数是通过与工作领域专家的访谈确定的。我们通过一个与水文勘测数据质量评估相关的案例研究,展示了该建议的意义所在。
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Data quality assessment through a preference model
Evaluating the quality of data is a problem of a multi-dimensional nature and quite frequently depends on the perspective of an expected use or final purpose of the data. Numerous works have explored the well-known specification of data quality dimensions in various application domains, without addressing the inter-dependencies and aggregation of quality attributes for decision support. In this work we therefore propose a context-dependent formal process to evaluate the quality of data which integrates a preference model from the field of Multi-Criteria Decision Aiding. The parameters of this preference model are determined through interviews with work-domain experts. We show the interest of the proposal on a case study related to the evaluation of the quality of hydrographical survey data.
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来源期刊
ACM Journal of Data and Information Quality
ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
4.10
自引率
4.80%
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0
期刊最新文献
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